Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

Cutting Edge: The ST2 Ligand IL-33 Potently Activates and Drives Maturation of Human Mast Cells

Abstract: IL-33, the natural ligand of the IL-1 receptor family member ST2L, is known to enhance experimental allergic-type inflammatory responses by costimulating the production of cytokines from activated Th2 lymphocytes. Although ST2L has long been known to be expressed by mast cells, its role in their biology has not been explored. In this study we report that IL-33 directly stimulates primary human mast cells (MCs) to produce several proinflammatory cytokines and chemokines and also exerts a permissive effect on the MCs response to thymic stromal lymphopoietin, a recently described potent MCs activator. IL-33 also acts both alone and in concert with thymic stromal lymphopoietin to accelerate the in vitro maturation of CD34 + MC precursors and induce the secretion of Th2 cytokines and Th2-attracting chemokines. Taken together, these results suggest that IL-33 may play an important role in mast cell-mediated inflammation and further emphasize the role of innate immunity in allergic diseases.

Deep Graph Infomax

Abstract: We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs—both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding.

Abstract: One of the key problems in spoken language understanding (SLU) is the task of slot filling. In light of the recent success of applying deep neural network technologies in domain detection and intent identification, we carried out an in-depth investigation on the use of recurrent neural networks for the more difficult task of slot filling involving sequence discrimination. In this work, we implemented and compared several important recurrent-neural-network architectures, including the Elman-type and Jordan-type recurrent networks and their variants. To make the results easy to reproduce and compare, we implemented these networks on the common Theano neural network toolkit, and evaluated them on the ATIS benchmark. We also compared our results to a conditional random fields (CRF) baseline. Our results show that on this task, both types of recurrent networks outperform the CRF baseline substantially, and a bi-directional Jordantype network that takes into account both past and future dependencies among slots works best, outperforming a CRFbased baseline by 14% in relative error reduction.

Induction of a non-encephalitogenic type 2 T helper-cell autoimmune response in multiple sclerosis after administration of an altered peptide ligand in a placebo- controlled, randomized phase II trial

Abstract: In this ‘double-blind’, randomized, placebo-controlled phase II trial, we compared an altered peptide ligand of myelin basic protein with placebo, evaluating their safety and influence on magnetic resonance imaging in relapsing–remitting multiple sclerosis. A safety board suspended the trial because of hypersensitivity reactions in 9% of the patients. There were no increases in either clinical relapses or in new enhancing lesions in any patient, even those with hypersensitivity reactions. Secondary analysis of those patients completing the study showed that the volume and number of enhancing lesions were reduced at a dose of 5 mg. There was also a regulatory type 2 T helper-cell response to altered peptide ligand that cross-reacted with the native peptide.

Searches for Lepton Flavor Violation in the Decays tau(+/-) -> e(+/-)gamma and tau(+/-) -> mu(+/-)gamma

Abstract: Searches for lepton-flavor-violating decays of a tau lepton to a lighter mass lepton and a photon have been performed with the entire data set of (963 +/- 7) x 10(6) tau decays collected by the BABAR detector near the Y(4S), Y(3S) and Y(2S) resonances. The searches yield no evidence of signals and we set upper limits on the branching fractions of B(tau(+/-) -> e(+/-)gamma) mu(+/-)gamma) < 4.4 X 10(-8) at 90% confidence level.